A method, system and medium for identifying and processing white moth based on big data

Abstract

The present application provides a method, system and medium for identifying and processing white moths based on big data, which belongs to the field of image recognition and big data technology. It includes: obtaining the image feature data and image feature data of white moths in the same historical period of the area to be identified and comparing them to obtain the image comparison similarity, and then combining the historical white moth outbreak rate data of multiple sub-areas to obtain the white moth high incidence prediction index, screening out the suspected high incidence sub-areas of white moths, and then processing the current environmental change data of the suspected high incidence sub-areas of white moths and the historical environmental average change data to obtain the white moth ring change induced contrast index, and then threshold comparison to determine whether the sub-area is a white moth high incidence prediction area, and issue an early warning; thereby realizing the identification and prediction of the high incidence of white moths in the region through image recognition and data processing.

Description

Technical Field

[0001] The present application relates to the field of image recognition and big data technology, and in particular to a method, system and medium for identifying and processing white moths based on big data. Background Art

[0002] In recent years, with global warming, the white moth outbreak has continued to spread southward. The white moth is a highly fertile and rapidly spreading pest. Its life cycle consists of four stages: egg, larva, pupa, and adult. The larval stage is long and the larvae are incredibly voracious. Larvae after the fifth instar, in particular, can devour vast quantities of leaves in a short period of time, causing severe damage to crops and forests.

[0003] The current white moth identification technology is mainly based on the observation of the growth status of white moths and the monitoring of white moths in areas where white moths have been found. However, these valuable experiences have not been well utilized to carry out further white moth identification and control. There is insufficient research on the impact of white moth growth environment. There is a lack of a technology that can predict the high incidence of white moths based on historical high incidence data of white moths and analyze and process the corresponding high incidence environment.

[0004] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0005] The purpose of the present application is to provide a method, system and medium for identifying and processing white moths based on big data. The method can obtain the white moth image feature data of the current time period of the area to be identified in multiple same historical time periods and the image feature data of multiple preset sub-areas, compare and obtain image feature comparison similarity data, obtain historical white moth outbreak rate data of multiple preset sub-areas, and process them in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index, compare them with a preset threshold, obtain a white moth suspected high incidence sub-area, obtain the current environmental change data of the white moth suspected high incidence sub-area, process them with the average data of multiple historical environmental change data, obtain a white moth ring change induced contrast index, further compare the threshold with the preset white moth ring change induced threshold, and mark it as a white moth high incidence prediction area if it meets the threshold, issue a white moth high incidence warning for the sub-area, and provide strong support for the white moth control plan; thereby, the identification and prediction of the high incidence of white moths in the region can be achieved through image recognition and data processing.

[0006] This application provides a method for identifying and processing white moths based on big data, comprising the following steps:

[0007] Obtain pest monitoring image sets for the current time period of the area to be identified in multiple identical historical time periods and extract multiple pest image feature data, and then classify them to obtain multiple types of pest image feature data sets;

[0008] Processing the plurality of types of insect pest monitoring image feature data sets through a preset insect pest category image feature recognition model to obtain white moth image feature data and corresponding white moth identification images;

[0009] Obtaining image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, and performing image feature similarity comparison with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region;

[0010] Obtaining historical white moth outbreak rate data for the plurality of preset sub-regions in the plurality of same historical time periods, and processing the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index corresponding to each sub-region;

[0011] Comparing the predicted index of high incidence of white moth corresponding to each sub-region with a preset prediction index threshold to obtain sub-regions suspected of high incidence of white moth;

[0012] Obtain multiple historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region, and compare the current environmental change data with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region;

[0013] A threshold comparison is performed based on the white moth ring change induction contrast index and the preset white moth ring change induction threshold, and the suspected high-incidence sub-area of ​​white moth that meets the threshold comparison requirements is marked as a white moth high-incidence prediction area, and a high-incidence warning of white moth in the sub-area is issued.

[0014] Among them, in the big data-based white moth identification and processing method described in the present application, it is characterized in that the acquisition of pest monitoring image sets in multiple historical time periods that are the same as the current time period of the area to be identified and the extraction of multiple pest image feature data are performed, and then the classification is performed to obtain multiple types of pest image feature data sets, specifically:

[0015] Obtaining a set of pest monitoring images that are in the same historical time period as the current time period of the area to be identified;

[0016] Extracting image feature information based on the pest monitoring image set to obtain pest image feature data corresponding to a plurality of historical pest monitoring images;

[0017] Classification processing is performed based on the pest image feature data corresponding to the multiple historical pest monitoring images to obtain multiple types of pest image feature data sets.

[0018] Among them, in the big data-based white moth identification and processing method described in the present application, it is characterized in that the image feature data of multiple preset sub-areas in the current time period of the area to be identified are obtained, and image feature similarity comparison is performed with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-area, specifically:

[0019] Acquire image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, including outline size identification data, texture pattern data, color distribution data, and structural space relationship data;

[0020] The image feature data of the preset sub-region is compared with the image feature data of the white moth to obtain image feature comparison similarity data corresponding to each sub-region.

[0021] Among them, in the big data-based white moth identification and processing method described in the present application, it is characterized in that the historical white moth outbreak rate data of the multiple preset sub-regions in the multiple same historical time periods are obtained, and the white moth high incidence prediction index corresponding to each sub-region is obtained by combining the corresponding image feature comparison similarity data for processing, specifically:

[0022] Acquire historical white moth outbreak rate data of the plurality of preset sub-areas in the plurality of same historical time periods;

[0023] The historical white moth outbreak rate data is combined with the corresponding image feature comparison similarity data and processed through a preset high-incidence prediction model to obtain a white moth high-incidence prediction index corresponding to each sub-region.

[0024] Among them, in the big data-based white moth identification and processing method described in the present application, it is characterized in that the white moth high incidence prediction index corresponding to each sub-area is compared with a preset prediction index threshold to obtain a suspected high-incidence sub-area of ​​white moth, specifically:

[0025] Comparing the white moth high incidence prediction index corresponding to each sub-region with a preset prediction index threshold to obtain a prediction index deviation rate;

[0026] Determining whether the prediction index deviation rate is greater than a preset prediction index deviation rate threshold;

[0027] If the prediction index deviation rate is greater than or equal to the preset prediction index deviation rate threshold, the current sub-region is not a high-incidence sub-region of white moth;

[0028] If the prediction index deviation rate is less than the preset prediction index deviation rate threshold, the current sub-region is a suspected high-incidence sub-region of white moth.

[0029] Among them, in the big data-based white moth identification and processing method described in the present application, it is characterized in that a plurality of historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region of white moths are obtained, and the current environmental change data are compared with the average data of the plurality of historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of white moths, specifically:

[0030] Obtain multiple historical environmental change data for the same historical time period when historical white moth outbreaks occurred in each suspected high-incidence sub-region, including historical temperature and humidity fluctuation data, historical light intensity change data, historical vegetation gnawing rate data, and historical precipitation accumulation data;

[0031] Obtaining current environmental change data for each suspected high-incidence sub-area of ​​white moths during the current time period, including temperature and humidity fluctuation data, light intensity change data, vegetation gnawing rate data, and precipitation accumulation data;

[0032] The current environmental change data were compared with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of the white moth;

[0033] The calculation formula of the white moth annular change induced contrast index is:

[0034]

[0035] Among them, E φ is the contrast index of the gypsy moth ring change, e bn is the current environmental change data, e hn is the average data of multiple historical environmental change data, κ1, κ z , μ1, μ2 are preset characteristic coefficients.

[0036] Among them, in the big data-based white moth identification and processing method described in the present application, it is characterized in that the threshold comparison is performed based on the white moth ring change induction contrast index and the preset white moth ring change induction threshold, and the suspected high-incidence sub-region of white moths that meets the threshold comparison requirements is marked as a white moth high-incidence prediction region, and a white moth high-incidence warning is performed in the sub-region, specifically:

[0037] Comparing the contrast index of the white moth ring change induction with a preset white moth ring change induction threshold to obtain a contrast index deviation rate;

[0038] Determining whether the contrast index deviation rate is greater than a preset contrast index deviation rate threshold;

[0039] If the contrast index deviation rate is greater than or equal to the preset contrast index deviation rate threshold, then the suspected high-incidence area of ​​white moths does not have a high-incidence situation of white moths;

[0040] If the contrast index deviation rate is less than the preset contrast index deviation rate threshold, the suspected area with high incidence of white moths is marked as a predicted area with high incidence of white moths and a sub-area high incidence warning of white moths is issued.

[0041] In a second aspect, the present application provides a white moth identification and processing system based on big data, the system comprising: a memory and a processor, the memory including a program of a white moth identification and processing method based on big data, the program of the white moth identification and processing method based on big data being executed by the processor to implement the following steps:

[0042] Obtain pest monitoring image sets for the current time period of the area to be identified in multiple identical historical time periods and extract multiple pest image feature data, and then classify them to obtain multiple types of pest image feature data sets;

[0043] Processing the plurality of types of insect pest monitoring image feature data sets through a preset insect pest category image feature recognition model to obtain white moth image feature data and corresponding white moth identification images;

[0044] Obtaining image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, and performing image feature similarity comparison with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region;

[0045] Obtaining historical white moth outbreak rate data for the plurality of preset sub-regions in the plurality of same historical time periods, and processing the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index corresponding to each sub-region;

[0046] Comparing the predicted index of high incidence of white moth corresponding to each sub-region with a preset prediction index threshold to obtain sub-regions suspected of high incidence of white moth;

[0047] Obtain multiple historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region, and compare the current environmental change data with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region;

[0048] A threshold comparison is performed based on the white moth ring change induction contrast index and the preset white moth ring change induction threshold, and the suspected high-incidence sub-area of ​​white moth that meets the threshold comparison requirements is marked as a white moth high-incidence prediction area, and a high-incidence warning of white moth in the sub-area is issued.

[0049] Among them, in the big data-based white moth identification and processing system described in the present application, it is characterized in that the acquisition of pest monitoring image sets in multiple historical time periods that are the same as the current time period of the area to be identified and the extraction of multiple pest image feature data sets, and then classification to obtain multiple types of pest image feature data sets, specifically:

[0050] Obtaining a set of pest monitoring images that are in the same historical time period as the current time period of the area to be identified;

[0051] Extracting image feature information based on the pest monitoring image set to obtain pest image feature data corresponding to a plurality of historical pest monitoring images;

[0052] Classification processing is performed based on the pest image feature data corresponding to the multiple historical pest monitoring images to obtain multiple types of pest image feature data sets.

[0053] On the third aspect, the present application also provides a readable storage medium, which includes a white moth identification and processing method program based on big data. When the white moth identification and processing method program based on big data is executed by a processor, the steps of the white moth identification and processing method based on big data as described in any one of the above items are implemented.

[0054] From the above, it can be seen that the present application provides a method, system and medium for identifying and processing white moths based on big data, which obtains the white moth image feature data of the current time period of the area to be identified in multiple identical historical time periods and the image feature data of multiple preset sub-areas, and compares to obtain image feature comparison similarity data, obtains historical white moth outbreak rate data of multiple preset sub-areas, and processes them in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index, compares it with a preset threshold, obtains a white moth suspected high incidence sub-area, obtains the current environmental change data of the white moth suspected high incidence sub-area, processes it with the average data of multiple historical environmental change data, obtains a white moth ring change induced contrast index, and further compares the threshold with the preset white moth ring change induced threshold. If the threshold is met, it is marked as a white moth high incidence. Prediction area, and make early warning of high incidence of white moth in sub-areas. This application not only takes into account the pest monitoring images of the current time period, but also combines multiple pest monitoring image sets of the same historical time period. By extracting and analyzing historical data, it can more comprehensively understand the laws and patterns of pest occurrence, thereby improving the accuracy and reliability of identification, and subdivide the area to be identified into multiple preset sub-areas. This regional subdivision strategy makes the early warning more accurate, and can specifically identify specific areas where white moth may be highly prevalent, which is conducive to the rational allocation of resources and effective response. It also considers the impact of historical environmental change data on the current prediction of high incidence of white moths, and calculates the white moth ring change induced contrast index, which further improves the scientificity and accuracy of the prediction, provides strong support for the white moth control plan, and provides data support for the formulation of scientific white moth pest management strategies.

[0055] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 A flowchart of a method for identifying and processing white moths based on big data provided in an embodiment of the present application;

[0058] Figure 2 A flowchart of a method for identifying and processing white moths based on big data, which provides a method for obtaining multiple types of insect pest image feature datasets, is provided in an embodiment of the present application;

[0059] Figure 3 A flowchart of obtaining image feature comparison similarity data corresponding to each sub-region in a big data-based white moth identification and processing method provided in an embodiment of the present application;

[0060] Figure 4 A flowchart of a method for identifying and processing white moths based on big data, for obtaining a white moth high incidence prediction index corresponding to each sub-region, provided in an embodiment of the present application;

[0061] Figure 5 A flowchart of obtaining suspected high-incidence areas of white moths in a big data-based white moth identification and processing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0063] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0064] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for identifying and processing white moths based on big data in some embodiments of the present application. The method for identifying and processing white moths based on big data is used in a terminal device, such as a computer, a mobile phone terminal, etc. The method for identifying and processing white moths based on big data includes the following steps:

[0065] S101, obtaining a set of pest monitoring images of the current time period of the area to be identified in multiple identical historical time periods, extracting multiple pest image feature data, and then classifying them to obtain multiple types of pest image feature data sets;

[0066] S102, processing the plurality of types of insect pest monitoring image feature data sets using a preset insect pest category image feature recognition model to obtain white moth image feature data and corresponding white moth identification images;

[0067] S103, obtaining image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, and performing image feature similarity comparison with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region;

[0068] S104, obtaining historical white moth outbreak rate data of the plurality of preset sub-regions in the plurality of same historical time periods, and processing the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index corresponding to each sub-region;

[0069] S105, comparing the predicted index of high incidence of white moth corresponding to each sub-region with a preset prediction index threshold to obtain a sub-region suspected to have high incidence of white moth;

[0070] S106, obtaining a plurality of historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region, and performing a comparison process based on the current environmental change data and the average data of the plurality of historical environmental change data to obtain a white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region;

[0071] S107, performing a threshold comparison between the white moth ring change induction contrast index and a preset white moth ring change induction threshold, marking the suspected high-incidence sub-region of white moths that meets the threshold comparison requirements as a white moth high-incidence prediction region, and issuing a white moth high-incidence warning for the sub-region.

[0072] Among them, the present application obtains the image feature data of white moths in the current time period of the area to be identified in multiple identical historical time periods and the image feature data of multiple preset sub-areas, compares and obtains the image feature comparison similarity data, obtains the historical white moth outbreak rate data of multiple preset sub-areas, and combines the corresponding image feature comparison similarity data for processing to obtain a white moth high incidence prediction index, compares it with a preset threshold, obtains a white moth suspected high incidence sub-area, obtains the current environmental change data of the white moth suspected high incidence sub-area, processes it with the average data of multiple historical environmental change data, obtains the white moth ring change induced contrast index, further compares it with the preset white moth ring change induced threshold, and marks it as a white moth high incidence prediction area if it meets the threshold, and performs white moth high incidence in the sub-area. Early warning. This application not only considers the pest monitoring images of the current time period, but also combines multiple pest monitoring image sets of the same historical time period. By extracting and analyzing historical data, it can more comprehensively understand the laws and patterns of pest occurrence, thereby improving the accuracy and reliability of identification. The area to be identified is subdivided into multiple preset sub-areas. This regional subdivision strategy makes the early warning more accurate and can specifically identify specific areas where white moths may occur frequently, which is conducive to the rational allocation of resources and effective response. It also considers the impact of historical environmental change data on the current prediction of high incidence of white moths, and calculates the white moth ring change induced contrast index, which further improves the scientificity and accuracy of the prediction, provides strong support for white moth control plans, and provides data support for the formulation of scientific white moth pest management strategies.

[0073] Please refer to Figure 2 , Figure 2 This is a flowchart of obtaining multiple types of insect pest image feature datasets for a big data-based white moth identification and processing method in some embodiments of the present application. According to an embodiment of the present invention, obtaining insect pest monitoring image sets that coincide with multiple historical time periods in the current time period of the area to be identified, extracting multiple types of insect pest image feature data, and then classifying them to obtain multiple types of insect pest image feature datasets is specifically as follows:

[0074] S201, obtaining a set of pest monitoring images that are in the same historical time period as the current time period of the area to be identified;

[0075] S202: Extracting image feature information from the pest monitoring image set to obtain pest image feature data corresponding to a plurality of historical pest monitoring images;

[0076] S203: performing classification processing based on the pest image feature data corresponding to the plurality of historical pest monitoring images to obtain a plurality of types of pest image feature data sets.

[0077] Among them, in order to extract the characteristic data of pests in the image, a set of pest monitoring images in multiple historical time periods that are the same as the current time period of the area to be identified is obtained. For example, a set of pest monitoring images covering multiple time periods (such as spring, summer, autumn, and winter) is obtained through a historical database, and fuzzy, repeated, and irrelevant images are checked and removed. Image feature information is extracted from the pest monitoring image set, such as the type, quantity, location, severity, etc. of pests. These feature information are organized into a structured data form to obtain pest image feature data corresponding to multiple historical pest monitoring images. The pest image feature data corresponding to the multiple historical pest monitoring images are classified and processed to obtain multiple types of pest image feature data sets.

[0078] Please refer to Figure 3 , Figure 3 This is a flowchart of obtaining image feature comparison and similarity data corresponding to each sub-region of a big data-based white moth identification and processing method in some embodiments of the present application. According to an embodiment of the present invention, the image feature data of multiple preset sub-regions in the current time period of the area to be identified are obtained, and image feature similarity comparison is performed with the white moth image feature data to obtain image feature comparison and similarity data corresponding to each sub-region, specifically as follows:

[0079] S301, obtaining image feature data of multiple preset sub-regions in the current time period of the area to be identified, including outline size identification data, texture pattern data, color distribution data and structural space relationship data;

[0080] S302 : performing image feature similarity comparison between the image feature data of the preset sub-region and the image feature data of the white moth to obtain image feature comparison similarity data corresponding to each sub-region.

[0081] Among them, in order to determine whether there are white moths in the area to be identified, the area to be identified is first divided into multiple preset sub-areas, and image feature data of the multiple preset sub-areas in the current time period of the area to be identified is obtained, including contour size identification data, texture pattern data, color distribution data and structural spatial relationship data. Contour size identification data refers to the edge shape and size information of the object or area in the image. This data is usually obtained through an edge detection algorithm and is used to describe the geometric properties of the object such as the outer contour shape, aspect ratio, area, etc. Texture pattern data describes the pixel arrangement pattern of the local area in the image, which is usually related to the physical properties of the object such as the material and surface roughness. Color distribution data refers to the distribution of different colors or color combinations in the image, including the type, number, position and spatial relationship between the colors. Structural spatial relationship data describes the spatial position, direction, distance and other relationships between objects or areas in the image. The image feature data of the preset sub-areas are compared with the white moth image feature data for image feature similarity to obtain the image feature comparison similarity data corresponding to each sub-area. It can be used to determine which preset sub-areas are similar to the white moth image and the degree of their similarity.

[0082] The calculation formula for the image feature comparison similarity data is:

[0083]

[0084] Among them, M ds is the image feature comparison similarity data, l c 、w n 、c t 、g k They are the outline size identification data, texture pattern data, color distribution data and structure space relationship data of the preset sub-area, cb 、w nb 、c tb 、g kb are the contour size identification data, texture pattern data, color distribution data and structural space relationship data of the white moth image respectively, and β, α1, α2, α3, α4, ξ1, ξ2, ξ3, ξ4 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset white moth identification parameter database).

[0085] Please refer to Figure 4 , Figure 4 This is a flowchart of a method for identifying and processing white moths based on big data in some embodiments of the present application for obtaining a white moth high incidence prediction index corresponding to each sub-region. According to an embodiment of the present invention, the method of obtaining historical white moth outbreak rate data for multiple preset sub-regions in multiple identical historical time periods and processing the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index corresponding to each sub-region is specifically as follows:

[0086] S401, obtaining historical white moth outbreak rate data for the plurality of preset sub-regions in the same plurality of historical time periods;

[0087] S402: Process the historical white moth outbreak rate data in combination with the corresponding image feature comparison similarity data through a preset high incidence prediction model to obtain a white moth high incidence prediction index corresponding to each sub-region.

[0088] In order to further determine whether there are white moths in the area to be identified, historical white moth outbreak rate data for multiple preset sub-areas in multiple identical historical time periods are obtained, and the actual white moth outbreaks in each sub-area during these time periods are recorded. The historical white moth outbreak rate data is combined with the corresponding image feature comparison similarity data and processed through a preset high-incidence prediction model to obtain a white moth high-incidence prediction index corresponding to each sub-area, indicating the possibility and severity of a white moth outbreak in each sub-area in a certain time period in the future;

[0089] The calculation formula of the high incidence prediction model is:

[0090]

[0091] Among them, G f is the high incidence prediction index of white moth, H b is the historical outbreak rate data of white moth, M ds is the image feature comparison similarity data, ∈, η, θ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset white moth identification parameter database).

[0092] Please refer to Figure 5 , Figure 5 This is a flowchart of obtaining sub-regions suspected of being high-incidence of white moths in a big data-based white moth identification and processing method in some embodiments of the present application. According to an embodiment of the present invention, the white moth high-incidence prediction index corresponding to each sub-region is compared with a preset prediction index threshold to obtain the sub-region suspected of being high-incidence of white moths, specifically:

[0093] S501, comparing the white moth high incidence prediction index corresponding to each sub-region with a preset prediction index threshold to obtain a prediction index deviation rate;

[0094] S502: Determine whether the prediction index deviation rate is greater than a preset prediction index deviation rate threshold;

[0095] S503: If the prediction index deviation rate is greater than or equal to the preset prediction index deviation rate threshold, the current sub-region is not a high-incidence sub-region of the white moth;

[0096] S504: If the prediction index deviation rate is less than the preset prediction index deviation rate threshold, the current sub-region is a suspected high-incidence sub-region of gypsy moth.

[0097] Among them, in order to further determine whether there is a high incidence of white moths in the area to be identified, the white moth high incidence prediction index corresponding to each sub-area is compared with the preset prediction index threshold to obtain the prediction index deviation rate, and determine whether the prediction index deviation rate is greater than the preset prediction index deviation rate threshold. If the prediction index deviation rate is greater than or equal to the preset prediction index deviation rate threshold, the current sub-area is not a sub-area with a high incidence of white moths. If the prediction index deviation rate is less than the preset prediction index deviation rate threshold, the current sub-area is a sub-area with a suspected high incidence of white moths.

[0098] According to an embodiment of the present invention, the method of obtaining a plurality of historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region of white moths, and performing a comparison process based on the current environmental change data and the average data of the plurality of historical environmental change data, obtains the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of white moths, specifically:

[0099] Obtain multiple historical environmental change data for the same historical time period when historical white moth outbreaks occurred in each suspected high-incidence sub-region, including historical temperature and humidity fluctuation data, historical light intensity change data, historical vegetation gnawing rate data, and historical precipitation accumulation data;

[0100] Obtaining current environmental change data for each suspected high-incidence sub-area of ​​white moths during the current time period, including temperature and humidity fluctuation data, light intensity change data, vegetation gnawing rate data, and precipitation accumulation data;

[0101] The current environmental change data were compared with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of the white moth;

[0102] The calculation formula of the white moth annular change induced contrast index is:

[0103]

[0104] Among them, E φ is the contrast index of the gypsy moth ring change, e bn is the current environmental change data, e hn is the average data of multiple historical environmental change data, and χ1, κ2, μ1, and μ2 are preset characteristic coefficients.

[0105] Among them, in order to evaluate the environmental characteristics and changes of historical white moth outbreaks in suspected high-incidence areas of white moths and compare them with the current environment, multiple historical environmental change data of the same historical time period when historical white moth outbreaks occurred in each suspected high-incidence sub-area of ​​white moths were obtained, including historical temperature and humidity fluctuation data, historical light intensity change data, historical vegetation gnawing rate data and historical precipitation accumulation data, and current environmental change data of each suspected high-incidence sub-area of ​​white moths in the current time period were obtained, including temperature and humidity fluctuation data, light intensity change data, vegetation gnawing rate data and precipitation accumulation data. The temperature and humidity fluctuation data include day and night temperature difference, humidity fluctuation, etc., because these factors directly affect the survival and reproduction of white moths. The light intensity change data records the changes in light intensity every day or in a specific period of time, which is an important factor in the life cycle of white moths and affects their activities and reproduction. The vegetation gnawing rate data is recorded by regularly surveying the damage to vegetation (such as the proportion of leaves gnawn), which directly reflects the degree of damage to vegetation by white moths. The precipitation accumulation data counts the cumulative precipitation in a specific time period, which affects the survival rate of white moth larvae and the migration ability of adults. By comparing the current environmental change data with the average data of multiple historical environmental change data, the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-area of ​​the white moth is obtained.

[0106] According to an embodiment of the present invention, a threshold comparison is performed based on the white moth ring change induction contrast index and a preset white moth ring change induction threshold, and a sub-region suspected of high incidence of white moths that meets the threshold comparison requirements is marked as a white moth high incidence prediction region, and a white moth high incidence warning is issued in the sub-region, specifically:

[0107] Comparing the contrast index of the white moth ring change induction with a preset white moth ring change induction threshold to obtain a contrast index deviation rate;

[0108] Determining whether the contrast index deviation rate is greater than a preset contrast index deviation rate threshold;

[0109] If the contrast index deviation rate is greater than or equal to the preset contrast index deviation rate threshold, then the suspected high-incidence area of ​​white moths does not have a high-incidence situation of white moths;

[0110] If the contrast index deviation rate is less than the preset contrast index deviation rate threshold, the suspected area with high incidence of white moths is marked as a predicted area with high incidence of white moths and a sub-area high incidence warning of white moths is issued.

[0111] Among them, in order to further judge the impact of environmental changes in areas suspected of high incidence of white moths on the growth and outbreak of white moths, the white moth ring change induction contrast index is compared with the preset white moth ring change induction threshold to obtain the contrast index deviation rate, and judge whether the contrast index deviation rate is greater than the preset contrast index deviation rate threshold. If the contrast index deviation rate is greater than or equal to the preset contrast index deviation rate threshold, there is no high incidence of white moths in the suspected high incidence area of ​​white moths. If the contrast index deviation rate is less than the preset contrast index deviation rate threshold, the suspected high incidence area of ​​white moths is marked as a high incidence prediction area of ​​white moths and a sub-area high incidence warning of white moths is issued.

[0112] According to an embodiment of the present invention, the further embodiment includes:

[0113] Extracting white moth identification image feature data according to the white moth identification image;

[0114] Extracting white moth growth cycle data according to the white moth identification image feature data, including egg stage growth data, larval stage growth data, pupal stage growth data and adult stage growth data;

[0115] The egg stage growth data, larval stage growth data, pupal stage growth data and adult stage growth data are processed by a preset white moth growth evaluation model to obtain a white moth growth rate index;

[0116] The calculation formula of the white moth growth evaluation model is:

[0117]

[0118] Among them, M r is the white moth growth rate index, l g 、y g 、b g 、h g are the egg stage growth data, larval stage growth data, pupal stage growth data and adult stage growth data, respectively; η1, η2, μ1, μ2 and π are the preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset white moth growth monitoring database).

[0119] Among them, in order to reflect the characteristics of each growth cycle of the white moth, the white moth growth cycle data is extracted according to the white moth identification image feature data, including egg stage growth data, such as egg size, color, shape, etc., larval stage growth data, such as body length, color change, screen shape, etc., pupal stage growth data, such as pupa size, color, shape, etc., adult stage growth data, such as wing color, number of spots, etc., and processed through the preset white moth growth evaluation model to obtain the white moth growth rate index, which is used to quantify the growth rate of the white moth, understand the growth rate of the white moth and the growth characteristics of different stages, predict the population outbreak time according to the growth rate, and formulate corresponding prevention and control strategies.

[0120] The present invention also discloses a white moth identification and processing system based on big data, comprising a memory and a processor. The memory comprises a white moth identification and processing method program based on big data. When the white moth identification and processing method program based on big data is executed by the processor, the following steps are implemented:

[0121] Obtain pest monitoring image sets for the current time period of the area to be identified in multiple identical historical time periods and extract multiple pest image feature data, and then classify them to obtain multiple types of pest image feature data sets;

[0122] Processing the plurality of types of insect pest monitoring image feature data sets through a preset insect pest category image feature recognition model to obtain white moth image feature data and corresponding white moth identification images;

[0123] Obtaining image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, and performing image feature similarity comparison with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region;

[0124] Obtaining historical white moth outbreak rate data for the plurality of preset sub-regions in the plurality of same historical time periods, and processing the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index corresponding to each sub-region;

[0125] Comparing the predicted index of high incidence of white moth corresponding to each sub-region with a preset prediction index threshold to obtain sub-regions suspected of high incidence of white moth;

[0126] Obtain multiple historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region, and compare the current environmental change data with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region;

[0127] A threshold comparison is performed based on the white moth ring change induction contrast index and the preset white moth ring change induction threshold, and the suspected high-incidence sub-area of ​​white moth that meets the threshold comparison requirements is marked as a white moth high-incidence prediction area, and a high-incidence warning of white moth in the sub-area is issued.

[0128] Among them, the present application obtains the image feature data of white moths in the current time period of the area to be identified in multiple identical historical time periods and the image feature data of multiple preset sub-areas, compares and obtains the image feature comparison similarity data, obtains the historical white moth outbreak rate data of multiple preset sub-areas, and combines the corresponding image feature comparison similarity data for processing to obtain a white moth high incidence prediction index, compares it with a preset threshold, obtains a white moth suspected high incidence sub-area, obtains the current environmental change data of the white moth suspected high incidence sub-area, processes it with the average data of multiple historical environmental change data, obtains the white moth ring change induced contrast index, further compares it with the preset white moth ring change induced threshold, and marks it as a white moth high incidence prediction area if it meets the threshold, and performs white moth high incidence in the sub-area. Early warning. This application not only considers the pest monitoring images of the current time period, but also combines multiple pest monitoring image sets of the same historical time period. By extracting and analyzing historical data, it can more comprehensively understand the laws and patterns of pest occurrence, thereby improving the accuracy and reliability of identification. The area to be identified is subdivided into multiple preset sub-areas. This regional subdivision strategy makes the early warning more accurate and can specifically identify specific areas where white moths may occur frequently, which is conducive to the rational allocation of resources and effective response. It also considers the impact of historical environmental change data on the current prediction of high incidence of white moths, and calculates the white moth ring change induced contrast index, which further improves the scientificity and accuracy of the prediction, provides strong support for white moth control plans, and provides data support for the formulation of scientific white moth pest management strategies.

[0129] According to an embodiment of the present invention, the method of obtaining a set of pest monitoring images in multiple historical time periods that are the same as the current time period of the area to be identified and extracting multiple pest image feature data, and then classifying them to obtain multiple types of pest image feature data sets, is specifically as follows:

[0130] Obtaining a set of pest monitoring images that are in the same historical time period as the current time period of the area to be identified;

[0131] Extracting image feature information based on the pest monitoring image set to obtain pest image feature data corresponding to a plurality of historical pest monitoring images;

[0132] Classification processing is performed based on the pest image feature data corresponding to the multiple historical pest monitoring images to obtain multiple types of pest image feature data sets.

[0133] Among them, in order to extract the characteristic data of pests in the image, a set of pest monitoring images in multiple historical time periods that are the same as the current time period of the area to be identified is obtained. For example, a set of pest monitoring images covering multiple time periods (such as spring, summer, autumn, and winter) is obtained through a historical database, and fuzzy, repeated, and irrelevant images are checked and removed. Image feature information is extracted from the pest monitoring image set, such as the type, quantity, location, severity, etc. of pests. These feature information are organized into a structured data form to obtain pest image feature data corresponding to multiple historical pest monitoring images. The pest image feature data corresponding to the multiple historical pest monitoring images are classified and processed to obtain multiple types of pest image feature data sets.

[0134] According to an embodiment of the present invention, the image feature data of multiple preset sub-regions in the current time period of the area to be identified are obtained, and image feature similarity comparison is performed with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region, specifically:

[0135] Acquire image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, including outline size identification data, texture pattern data, color distribution data, and structural space relationship data;

[0136] The image feature data of the preset sub-region is compared with the image feature data of the white moth to obtain image feature comparison similarity data corresponding to each sub-region.

[0137] Among them, in order to determine whether there are white moths in the area to be identified, the area to be identified is first divided into multiple preset sub-areas, and image feature data of the multiple preset sub-areas in the current time period of the area to be identified is obtained, including contour size identification data, texture pattern data, color distribution data and structural spatial relationship data. Contour size identification data refers to the edge shape and size information of the object or area in the image. This data is usually obtained through an edge detection algorithm and is used to describe the geometric properties of the object such as the outer contour shape, aspect ratio, area, etc. Texture pattern data describes the pixel arrangement pattern of the local area in the image, which is usually related to the physical properties of the object such as the material and surface roughness. Color distribution data refers to the distribution of different colors or color combinations in the image, including the type, number, position and spatial relationship between the colors. Structural spatial relationship data describes the spatial position, direction, distance and other relationships between objects or areas in the image. The image feature data of the preset sub-areas are compared with the white moth image feature data for image feature similarity to obtain the image feature comparison similarity data corresponding to each sub-area. It can be used to determine which preset sub-areas are similar to the white moth image and the degree of their similarity.

[0138] The calculation formula for the image feature comparison similarity data is:

[0139]

[0140] Among them, M ds is the image feature comparison similarity data, l c 、w n 、c t 、g k They are the outline size identification data, texture pattern data, color distribution data and structure space relationship data of the preset sub-area, cb 、w nb 、c tb 、g kb are the contour size identification data, texture pattern data, color distribution data and structural space relationship data of the white moth image respectively, and β, α1, α2, α3, α4, ξ1, ξ2, ξ3, ξ4 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset white moth identification parameter database).

[0141] According to an embodiment of the present invention, the historical white moth outbreak rate data of the multiple preset sub-regions in the multiple same historical time periods are obtained, and the data are processed in combination with the corresponding image feature comparison similarity data to obtain the white moth high incidence prediction index corresponding to each sub-region, specifically:

[0142] Acquire historical white moth outbreak rate data of the plurality of preset sub-areas in the plurality of same historical time periods;

[0143] The historical white moth outbreak rate data is combined with the corresponding image feature comparison similarity data and processed through a preset high-incidence prediction model to obtain a white moth high-incidence prediction index corresponding to each sub-region.

[0144] In order to further determine whether there are white moths in the area to be identified, historical white moth outbreak rate data for multiple preset sub-areas in multiple identical historical time periods are obtained, and the actual white moth outbreaks in each sub-area during these time periods are recorded. The historical white moth outbreak rate data is combined with the corresponding image feature comparison similarity data and processed through a preset high-incidence prediction model to obtain a white moth high-incidence prediction index corresponding to each sub-area, indicating the possibility and severity of a white moth outbreak in each sub-area in a certain time period in the future;

[0145] The calculation formula of the high incidence prediction model is:

[0146]

[0147] Among them, G f is the high incidence prediction index of white moth, H b is the historical outbreak rate data of white moth, Mds is the image feature comparison similarity data, ∈, η, θ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset white moth identification parameter database).

[0148] According to an embodiment of the present invention, the prediction index of high incidence of white moth corresponding to each sub-region is compared with a preset prediction index threshold to obtain a sub-region suspected to have high incidence of white moth, specifically:

[0149] Comparing the white moth high incidence prediction index corresponding to each sub-region with a preset prediction index threshold to obtain a prediction index deviation rate;

[0150] Determining whether the prediction index deviation rate is greater than a preset prediction index deviation rate threshold;

[0151] If the prediction index deviation rate is greater than or equal to the preset prediction index deviation rate threshold, the current sub-region is not a high-incidence sub-region of white moth;

[0152] If the prediction index deviation rate is less than the preset prediction index deviation rate threshold, the current sub-region is a suspected high-incidence sub-region of white moth.

[0153] Among them, in order to further determine whether there is a high incidence of white moths in the area to be identified, the white moth high incidence prediction index corresponding to each sub-area is compared with the preset prediction index threshold to obtain the prediction index deviation rate, and determine whether the prediction index deviation rate is greater than the preset prediction index deviation rate threshold. If the prediction index deviation rate is greater than or equal to the preset prediction index deviation rate threshold, the current sub-area is not a sub-area with a high incidence of white moths. If the prediction index deviation rate is less than the preset prediction index deviation rate threshold, the current sub-area is a sub-area with a suspected high incidence of white moths.

[0154] According to an embodiment of the present invention, the method of obtaining a plurality of historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region of white moths, and performing a comparison process based on the current environmental change data and the average data of the plurality of historical environmental change data, obtains the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of white moths, specifically:

[0155] Obtain multiple historical environmental change data for the same historical time period when historical white moth outbreaks occurred in each suspected high-incidence sub-region, including historical temperature and humidity fluctuation data, historical light intensity change data, historical vegetation gnawing rate data, and historical precipitation accumulation data;

[0156] Obtaining current environmental change data for each suspected high-incidence sub-area of ​​white moths during the current time period, including temperature and humidity fluctuation data, light intensity change data, vegetation gnawing rate data, and precipitation accumulation data;

[0157] The current environmental change data were compared with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of the white moth;

[0158] The calculation formula of the white moth annular change induced contrast index is:

[0159]

[0160] Among them, E φ is the contrast index of the gypsy moth ring change, e bn is the current environmental change data, e hn is the average data of multiple historical environmental change data, κ1, κ z , μ1, μ2 are preset characteristic coefficients.

[0161] Among them, in order to evaluate the environmental characteristics and changes of historical white moth outbreaks in suspected high-incidence areas of white moths and compare them with the current environment, multiple historical environmental change data of the same historical time period when historical white moth outbreaks occurred in each suspected high-incidence sub-area of ​​white moths were obtained, including historical temperature and humidity fluctuation data, historical light intensity change data, historical vegetation gnawing rate data and historical precipitation accumulation data, and current environmental change data of each suspected high-incidence sub-area of ​​white moths in the current time period were obtained, including temperature and humidity fluctuation data, light intensity change data, vegetation gnawing rate data and precipitation accumulation data. The temperature and humidity fluctuation data include day and night temperature difference, humidity fluctuation, etc., because these factors directly affect the survival and reproduction of white moths. The light intensity change data records the changes in light intensity every day or in a specific period of time, which is an important factor in the life cycle of white moths and affects their activities and reproduction. The vegetation gnawing rate data is recorded by regularly surveying the damage to vegetation (such as the proportion of leaves gnawn), which directly reflects the degree of damage to vegetation by white moths. The precipitation accumulation data counts the cumulative precipitation in a specific time period, which affects the survival rate of white moth larvae and the migration ability of adults. By comparing the current environmental change data with the average data of multiple historical environmental change data, the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-area of ​​the white moth is obtained.

[0162] According to an embodiment of the present invention, a threshold comparison is performed based on the white moth ring change induction contrast index and a preset white moth ring change induction threshold, and a sub-region suspected of high incidence of white moths that meets the threshold comparison requirements is marked as a white moth high incidence prediction region, and a white moth high incidence warning is issued in the sub-region, specifically:

[0163] Comparing the contrast index of the white moth ring change induction with a preset white moth ring change induction threshold to obtain a contrast index deviation rate;

[0164] Determining whether the contrast index deviation rate is greater than a preset contrast index deviation rate threshold;

[0165] If the contrast index deviation rate is greater than or equal to the preset contrast index deviation rate threshold, then the suspected high-incidence area of ​​white moths does not have a high-incidence situation of white moths;

[0166] If the contrast index deviation rate is less than the preset contrast index deviation rate threshold, the suspected area with high incidence of white moths is marked as a predicted area with high incidence of white moths and a sub-area high incidence warning of white moths is issued.

[0167] Among them, in order to further judge the impact of environmental changes in areas suspected of high incidence of white moths on the growth and outbreak of white moths, the white moth ring change induction contrast index is compared with the preset white moth ring change induction threshold to obtain the contrast index deviation rate, and judge whether the contrast index deviation rate is greater than the preset contrast index deviation rate threshold. If the contrast index deviation rate is greater than or equal to the preset contrast index deviation rate threshold, there is no high incidence of white moths in the suspected high incidence area of ​​white moths. If the contrast index deviation rate is less than the preset contrast index deviation rate threshold, the suspected high incidence area of ​​white moths is marked as a high incidence prediction area of ​​white moths and a sub-area high incidence warning of white moths is issued.

[0168] According to an embodiment of the present invention, the further embodiment includes:

[0169] Extracting white moth identification image feature data according to the white moth identification image;

[0170] Extracting white moth growth cycle data according to the white moth identification image feature data, including egg stage growth data, larval stage growth data, pupal stage growth data and adult stage growth data;

[0171] The egg stage growth data, larval stage growth data, pupal stage growth data and adult stage growth data are processed by a preset white moth growth evaluation model to obtain a white moth growth rate index;

[0172] The calculation formula of the white moth growth evaluation model is:

[0173]

[0174] Among them, M r is the white moth growth rate index, l g 、y g 、b g 、h g are the egg stage growth data, larval stage growth data, pupal stage growth data and adult stage growth data, respectively; η1, η2, μ1, μ2 and π are the preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset white moth growth monitoring database).

[0175] Among them, in order to reflect the characteristics of each growth cycle of the white moth, the white moth growth cycle data is extracted according to the white moth identification image feature data, including egg stage growth data, such as egg size, color, shape, etc., larval stage growth data, such as body length, color change, screen shape, etc., pupal stage growth data, such as pupa size, color, shape, etc., adult stage growth data, such as wing color, number of spots, etc., and processed through the preset white moth growth evaluation model to obtain the white moth growth rate index, which is used to quantify the growth rate of the white moth, understand the growth rate of the white moth and the growth characteristics of different stages, predict the population outbreak time according to the growth rate, and formulate corresponding prevention and control strategies; thereby, the high incidence of white moths in the region can be identified and predicted through image recognition and data processing.

[0176] The third aspect of the present invention also provides a readable storage medium, which includes a program for a white moth identification and processing method based on big data. When the program for a white moth identification and processing method based on big data is executed by a processor, the steps of a white moth identification and processing method based on big data as described in any one of the above items are implemented.

[0177] The present invention discloses a method, system and medium for identifying and processing white moths based on big data. The method comprises the following steps: obtaining white moth image feature data of a current time period of an area to be identified in multiple same historical time periods and image feature data of multiple preset sub-areas, comparing and obtaining image feature comparison similarity data, obtaining historical white moth outbreak rate data of multiple preset sub-areas, and processing the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index, comparing the data with a preset threshold value, obtaining a white moth suspected high incidence sub-area, obtaining current environmental change data of the white moth suspected high incidence sub-area, processing the data with the average data of multiple historical environmental change data, obtaining a white moth ring change induced contrast index, further performing a threshold comparison with a preset white moth ring change induced threshold value, and marking the area as a white moth high incidence prediction area if the threshold value is met. domain, and conduct early warning of high incidence of white moths in sub-regions. This application not only considers the pest monitoring images of the current time period, but also combines multiple pest monitoring image sets of the same historical time period. By extracting and analyzing historical data, it can more comprehensively understand the laws and patterns of pest occurrence, thereby improving the accuracy and reliability of identification, and subdivide the area to be identified into multiple preset sub-regions. This regional subdivision strategy makes the early warning more accurate, and can specifically identify specific areas where white moths may be highly prevalent, which is conducive to the rational allocation of resources and effective response. It also considers the impact of historical environmental change data on the current prediction of high incidence of white moths, and calculates the white moth ring change induced contrast index, which further improves the scientificity and accuracy of the prediction, provides strong support for white moth control plans, and provides data support for the formulation of scientific white moth pest management strategies.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0179] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0180] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0181] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0182] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for identifying and processing white moths based on big data, characterized in that: The following steps are involved: Obtaining a set of pest monitoring images of the current time period of the area to be identified in multiple same historical time periods and extracting multiple pest image feature data, and then classifying them to obtain multiple types of pest monitoring image feature data sets; Processing the plurality of types of pest monitoring image feature data sets through a preset pest category image feature recognition model to obtain white moth image feature data and corresponding white moth identification images; Acquire image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, and perform image feature similarity comparison with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region; Acquire historical white moth outbreak rate data of the plurality of preset sub-areas in the plurality of same historical time periods, and process the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index corresponding to each sub-area; Compare the white moth high incidence prediction index corresponding to each sub-area with a preset prediction index threshold to obtain a suspected white moth high incidence sub-area; Obtain multiple historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region of white moth, and compare and process the current environmental change data with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of white moth; A threshold comparison is performed based on the white moth ring change induction contrast index and the preset white moth ring change induction threshold, and the suspected high-incidence sub-area of ​​white moth that meets the threshold comparison requirements is marked as a white moth high-incidence prediction area, and a high-incidence warning of white moth in the sub-area is issued.

2. A method for identifying and processing white moths based on big data according to claim 1, characterized in that: The method of obtaining a set of pest monitoring images of the current time period of the area to be identified in multiple same historical time periods and extracting multiple pest image feature data, and then classifying to obtain multiple types of pest monitoring image feature data sets, is specifically: Acquire a set of pest monitoring images in multiple historical time periods that are the same as the current time period of the area to be identified; Extracting image feature information according to the pest monitoring image set to obtain pest image feature data corresponding to a plurality of historical pest monitoring images; Classification processing is performed according to the pest image feature data corresponding to the multiple historical pest monitoring images to obtain multiple types of pest monitoring image feature data sets.

3. A method for identifying and processing white moths based on big data according to claim 2, characterized in that: The acquiring of the image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, and performing image feature similarity comparison with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region, is specifically: Acquire image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, including contour size identification data, texture pattern data, color distribution data and structure space relationship data; The image feature data of the preset sub-region is compared with the image feature data of the white moth to obtain image feature comparison similarity data corresponding to each sub-region.

4. A method for identifying and processing white moths based on big data according to claim 3, characterized in that: The historical white moth outbreak rate data of the plurality of preset sub-areas in the plurality of same historical time periods are obtained, and the white moth high incidence prediction index corresponding to each sub-area is obtained by combining the corresponding image feature comparison similarity data for processing, specifically: Acquire historical white moth outbreak rate data of the plurality of preset sub-areas in the plurality of same historical time periods; The historical white moth outbreak rate data is combined with the corresponding image feature comparison similarity data and processed through a preset high incidence prediction model to obtain a white moth high incidence prediction index corresponding to each sub-region.

5. A method for identifying and processing white moths based on big data according to claim 4, characterized in that: The white moth high incidence prediction index corresponding to each sub-area is compared with a preset prediction index threshold to obtain a sub-area suspected of high incidence of white moth, specifically: Compare the white moth high incidence prediction index corresponding to each sub-area with a preset prediction index threshold to obtain a prediction index deviation rate; Determining whether the prediction index deviation rate is greater than a preset prediction index deviation rate threshold; If the prediction index deviation rate is greater than or equal to the preset prediction index deviation rate threshold, the current sub-region is not a high-incidence sub-region of white moth; If the prediction index deviation rate is less than the preset prediction index deviation rate threshold, the current sub-region is a suspected high-incidence sub-region of white moth.

6. A method for identifying and processing white moths based on big data according to claim 5, characterized in that: The method obtains a plurality of historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region of white moth, and compares the current environmental change data with the average data of the plurality of historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of white moth, specifically: Obtain multiple historical environmental change data for the same historical time period when historical white moth outbreaks occurred in each suspected high-incidence sub-area, including historical temperature and humidity fluctuation data, historical light intensity change data, historical vegetation gnawing rate data, and historical precipitation accumulation data; Obtaining the current environmental change data of each suspected high-incidence sub-area of ​​white moth in the current time period, including temperature and humidity fluctuation rate data, light intensity change data, vegetation gnawing rate data and precipitation accumulation data; By comparing the current environmental change data with the average data of multiple historical environmental change data, the contrast index of the white moth ring change induction corresponding to each suspected high-incidence sub-area of ​​the white moth is obtained; The calculation formula of the contrast index induced by the white moth ring change is: ； in, is the contrast index induced by the white moth ring change, is the current environmental change data, is the average data of multiple historical environmental change data, is the preset characteristic coefficient.

7. A method for identifying and processing white moths based on big data according to claim 6, characterized in that: The threshold comparison is performed based on the white moth ring change induction contrast index and the preset white moth ring change induction threshold, and the suspected high-incidence sub-region of white moths that meets the threshold comparison requirements is marked as a white moth high-incidence prediction region, and a white moth high-incidence warning is issued in the sub-region, specifically: The contrast index deviation rate is obtained by comparing the white moth ring change induction contrast index with a preset white moth ring change induction threshold; Determining whether the contrast index deviation rate is greater than a preset contrast index deviation rate threshold; If the contrast index deviation rate is greater than or equal to the preset contrast index deviation rate threshold, then the suspected high-incidence sub-area of ​​white moths does not have a high-incidence situation of white moths; If the contrast index deviation rate is less than a preset contrast index deviation rate threshold, the sub-region suspected of high incidence of white moths is marked as a predicted high incidence of white moths area and a high incidence warning of white moths in the sub-region is issued.

8. A white moth identification and processing system based on big data, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a white moth identification and processing method program based on big data, and the white moth identification and processing method program based on big data is executed by the processor to implement the following steps: Obtaining a set of pest monitoring images of the current time period of the area to be identified in multiple same historical time periods and extracting multiple pest image feature data, and then classifying them to obtain multiple types of pest monitoring image feature data sets; Processing the plurality of types of pest monitoring image feature data sets through a preset pest category image feature recognition model to obtain white moth image feature data and corresponding white moth identification images; Acquire image feature data of a plurality of preset sub-regions in the current time period of the area to be identified, and perform image feature similarity comparison with the white moth image feature data to obtain image feature comparison similarity data corresponding to each sub-region; Acquire historical white moth outbreak rate data of the plurality of preset sub-areas in the plurality of same historical time periods, and process the data in combination with the corresponding image feature comparison similarity data to obtain a white moth high incidence prediction index corresponding to each sub-area; Compare the white moth high incidence prediction index corresponding to each sub-area with a preset prediction index threshold to obtain a suspected white moth high incidence sub-area; Obtain multiple historical environmental change data of historical white moth outbreaks in each suspected high-incidence sub-region of white moth, and compare and process the current environmental change data with the average data of multiple historical environmental change data to obtain the white moth ring change induced contrast index corresponding to each suspected high-incidence sub-region of white moth; A threshold comparison is performed based on the white moth ring change induction contrast index and a preset white moth ring change induction threshold, and sub-regions with suspected high incidence of white moths that meet the threshold comparison requirements are marked as white moth high incidence prediction areas, and a white moth high incidence warning is issued in the sub-regions.

9. A white moth identification and processing system based on big data according to claim 8, characterized in that: The method of obtaining a pest monitoring image set of the current time period of the area to be identified in multiple same historical time periods and extracting multiple pest image feature data, and then classifying to obtain multiple types of pest monitoring image feature data sets, is specifically: Acquire a set of pest monitoring images in multiple historical time periods that are the same as the current time period of the area to be identified; Extracting image feature information according to the pest monitoring image set to obtain pest image feature data corresponding to a plurality of historical pest monitoring images; Classification processing is performed according to the pest image feature data corresponding to the multiple historical pest monitoring images to obtain multiple types of pest monitoring image feature data sets.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a method for identifying and processing white moths based on big data and a medium program. When the method for identifying and processing white moths based on big data and the medium program are executed by a processor, the steps of a method for identifying and processing white moths based on big data as described in any one of claims 1 to 7 are implemented.

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